AI adoption is accelerating across organizations, and spending often follows a similar pattern: rapid growth, multiple providers, and limited visibility into where costs originate. Each provider exposes billing data differently, with distinct schemas, dimensions, and interfaces. FinOps and engineering teams often spend significant time consolidating fragmented data, only to end up with partial attribution and limited context about who or what generated the AI spending.
With the AI Costs feature, Datadog Cloud Cost Management (CCM) introduces a unified approach to AI cost visibility and attribution across providers such as OpenAI, Anthropic, GitHub Copilot, Amazon Bedrock, Google Gemini, and Vertex AI. Instead of reconciling separate billing and usage exports, teams can analyze AI spend alongside infrastructure costs, apply consistent tagging across providers, and map usage back to the users and services that generated it. CCM provides a single platform that replaces manual workflows and offers a clearer picture of how AI usage translates into cost.
In this post, we’ll explore how to:
Gain unified visibility of AI spend across providersStandardize tagging across providers in the CCM ExplorerAutomatically attribute AI spend to specific usersCreate reports that enhance business accountability
